Computerized modeling of electronic devices allows for inexpensive and efficient computerized design of electric circuits and systems. A discrete electronic device such as a bipolar transistor requires a large number of parameters to accurately model its operational characteristics. For example, a typical bipolar transistor using the SPICE modeling system requires on the order of thirty parameters to sufficiently specify its operational characteristics.
Once the parameters of an electronic device have been calculated so that the electronic device can be modeled, computer aided design systems can efficiently design circuits using the electronic device and use the calculated parameters to accurately predict the operational characteristics of the circuit comprising the device. In order to calculate the thirty or so parameters used to model an electronic device such as a bipolar transistor, the device is tested at various points in its operating spectrum to construct a data set of measured data values. A first guess is then made at the values for the modeling parameters of the device. An objective function is then calculated which represents a summation of the error between the measured and calculated data. Using an iterative process, the parameters are changed to minimize the value of the objective function. When the objective function is minimized, the values for the modeling parameters represent the closest approximation of the modeled parameters to the actual measured parameters.
A problem occurs within the objective function as many of the data sets have absolute values which are very small compared to the absolute values of other of the data sets. As such, when the objective function is calculated, the terms having smaller absolute values will not be significant as compared to the terms having larger absolute values. An additional problem inheres in the fact that a single term within the objective function may be associated with a data set which has a wide range of values within the data set. Accordingly, a single data point which has an absolute value significantly less than another data point within the same data set will not be accorded the same weight in the calculation of the objective function. Accordingly, some prior systems have incorporated normalization schemes within each term so that each data point within the objective function is accorded equal weight.
A problem occurs, however, when data points are included which are not modeled well and given the same relative weighting as data points which are modeled well. Accordingly, a need has arisen for a modeling system which can implement variable weighting of the data sets to provide the most accurate modeling of a particular device.